Localised surface plasmon-like resonance generated by microwave electromagnetic waves in pipe defects

Alobaidi, Wissam M.; Nima, Zeid A.; Sandgren, Eric P.

doi:10.1080/10589759.2017.1311331

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…In this paper, three different structures of sensors are simulated with the prism material BK7. The refractive index of BK7 prism varies with the wavelength by the following formula [ 39 ]:

…”

Section: Theoretical Analysis and Device Modeling And Simulation Setupmentioning

confidence: 99%

Design and Simulation of Au/SiO2 Nanospheres Based on SPR Refractive Index Sensor

Sun

Song

et al. 2023

Sensors

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In this paper, three different structures of surface plasmon resonance (SPR) sensors based on the Kretschmann configuration: Au/SiO2 thin film structure, Au/SiO2 nanospheres and Au/SiO2 nanorods are designed by adding three different forms of SiO2 materials behind the gold film of conventional Au-based SPR sensors. The effects of SiO2 shapes on the SPR sensor are investigated through modeling and simulation with the refractive index of the media to be measured ranging from 1.330 to 1.365. The results show that the sensitivity of Au/SiO2 nanospheres could be as high as 2875.4 nm/RIU, which is 25.96% higher than that of the sensor with a gold array. More interestingly, the increase in sensor sensitivity is attributed to the change in SiO2 material morphology. Therefore, this paper mainly explores the influence of the shape of the sensor-sensitizing material on the performance of the sensor.

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“…In this paper, three different structures of sensors are simulated with the prism material BK7. The refractive index of BK7 prism varies with the wavelength by the following formula [ 39 ]:

…”

Section: Theoretical Analysis and Device Modeling And Simulation Setupmentioning

confidence: 99%

Design and Simulation of Au/SiO2 Nanospheres Based on SPR Refractive Index Sensor

Sun

Song

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Microwaves-based detection methods have gained strong interest in recently because they could penetrate insulating materials, which makes them a great candidate for inspecting dielectric composite structures [3,15]. Microwave nondestructive testing and evaluation techniques are also efficient for dielectric pipeline inspection [3,16,17]. For pipeline coatings, a planar microstrip microwave ring resonator was designed to detect coating breaching by measuring the varying gap heights between a metal pipe's outer surface and the coating layer [2], and a cost-effective microwave sensor was used to detect defects primarily driven by coating failure [1].…”

Section: Introductionmentioning

confidence: 99%

Non-destructive evaluation of pipes by microwave techniques and artificial neural networks

Xie¹,

Yang²,

Yuan³

et al. 2020

Meas. Sci. Technol.

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Near-field imaging based on an electromagnetic sensor has been widely used for nondestructive detection. An approach to detect the near-surface defects in pipeline coatings and dielectric pipelines is proposed. Based on the characteristics of resonant frequency shifts, a novel method using artificial neural network (ANN) is established to quantitatively evaluate circular-section shape defects in pipes, such as air bubbles in pipeline coating layers or qualitative characterize non-circular section-shape defects. The proposed method has three important modules: a new resonator for data acquisition, a signal-processing algorithm for data preprocessing, and an ANN for quantitative imaging. In the designed sensor, we extend the tip of the sensing ring and introduce an appending in the ring gap for high sensitivity. Simulations show that the sensor can detect a defect with a radius as small as 0.7 mm. The raw resonant frequency shifts obtained by the sensor scanning at an angle interval around the specimen first are preprocessed by curve fitting, sampling, and adaptive data interpolation or truncation. Then, using an ANN, the relationships among resonant frequency shifts, external radius of the specimen, and defect size are modeled for imaging of circular-section shape defects. Preliminary simulations and measurements illustrate the efficacy of the method. Consequently, a contactless, high-resolution, near-field imaging measurement based on sensor scanning for inspecting pipe structures is obtained.

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Development of an Optimized Neural Network for the Detection of Pipe Defects Using a Microwave Signal

Alobaidi

Alkuam

Sandgren

2018

Journal of Pressure Vessel Technology

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Neural network technology is applied to the detection of a pipe wall thinning (PWT) in a pipe using a microwave signal reflection as an input. The location, depth, length, and profile geometry of the PWT are predicted by the neural network from input parameters taken from the resonance frequency plots for training data generated through computer simulation. The network is optimized using an evolutionary optimization routine, using the 108 training data samples to minimize the errors produced by the neural network model. The optimizer specified not only the optimal weights for the network links but also the optimal topology for the network itself. The results demonstrate the potential of the approach in that when data files were input that were not part of the training data set, fairly accurate predictions were made by the network. The results from the initial network models can be utilized to improve the future performance of the network.

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Localised surface plasmon-like resonance generated by microwave electromagnetic waves in pipe defects

Cited by 3 publications

References 20 publications

Design and Simulation of Au/SiO2 Nanospheres Based on SPR Refractive Index Sensor

Design and Simulation of Au/SiO2 Nanospheres Based on SPR Refractive Index Sensor

Non-destructive evaluation of pipes by microwave techniques and artificial neural networks

Development of an Optimized Neural Network for the Detection of Pipe Defects Using a Microwave Signal

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